Monday, April 22, 2013

A recent publication by RT Jones, L.G. Sanchez, and N Feirer:
"A cross-taxon analysis of insect-associated bacterial diversity"
started me thinking about how we sample insects. You can see the article
here at PLoSONE:

In this publication, the authors perform a large collection of
diverse insects and then subsequently a 16S rRNA gene amplicon analysis.
They use the V1-V2 region and 454 pyrosequencing. They use whole
insects as the source of material for the DNA extractions and perform a set of
bioinformatics processing steps downstream in an attempt to reduce artifacts
that may lead to inflation of diversity estimates in their samples. From their published methods, this seems to
be the order in which reads were processed:

1. Reads are truncated (to compare across the same
16S positions) and removed “low quality reads” using QIIME’s default settings
– I am not sure if amplicon noise removal was used as implemented therein nor
did they mention any alignment to any 16S model.

2. Sequences are binned at 97% identity using
uclust and the authors picked the most abundant sequence for downstream
analyses

3. Then, phylotypes
that did not represent at least 1% of community membership within the
sample were removed. I
should point out that from their methods, at this point, it seems that each
sample is a different total size so that this 1% will necessarily be a
different total number of sequences removed from each.

4. Any sample that does that have at least 500
sequences is removed.
Perhaps because that number was small –
they say the minimum was 61 in some cases.

In the end, across all samples, they were left with 477 unique
“phylotypes” (read: 97% identity OTUs).
Their goals in this study were to identify links between diet and
microbiome composition and also analyze the diversity of microbes found
associated with insects of different, diverse genera. They do not find any effect of diet, at least
based on their taxonomic classification of hosts (morphological) and published
understandings of what these hosts might consume. See Figure 2 from the paper below. Let’s explore whether or not, given the
sampling, this a surprising result.

Figure 2. Bray-Curtis cluster of insect
species based on their associated bacterial communities (all 477 bacterial
phylotypes used for Bray-Curtis analysis) and Z-scores of the 96 most abundant
bacterial phylotypes with lowest scores in light blue and highest scores in
dark blue.

Each column is a unique bacterial phylotype.
Phylotypes are arranged according to taxonomic classification. Insect species
are identified by a four-letter code (Table 1) with the first letter indicating the
order, as follows: (C) Coleoptera, (D) Diptera, (H) Hemiptera, (I) Isoptera,
(L) Lepidoptera, (N) Neuroptera, (S) Siphonoptera, and (Y) Hymenoptera.

doi:10.1371/journal.pone.0061218.g002

Sampling
of insects

How many bacterial cells do you suppose cover the surfaces of a
single insect? How many live within their cells in specialized organs or within
their reproductive tracts? Estimates
based on microscopic observations and qPCR data on single copy functional genes
suggest that insects infected with bacterial symbionts harbor upwards of 109
bacterial cells (for that one,
specific symbiont) [1, 2] and indeed, 200,000 Buchnera are estimated to be transmitted
in each single generation [3]. Ok, so if your insect happens to be infected with
a bacterial symbiont, your dataset will include this organism in abundance (as
found by the study reviewed here for several of their samples – some of which
were >90% Wolbachia). Also, these kinds of symbioses tend to be very
species specific – a single rRNA gene phylotype is likely to be found for these
bacteria. How many other bacterial cells
do you think there are on and in this host?
Do you think you can effectively sample them in the presence of an
over-abundant single phylotype?

Now let’s consider the fact that when most of us sample insects,
we grind up the entire body. This isn’t just a laziness issue, it’s due to the
fact that insects are small, difficult to dissect, and often preserved in ways
that make it even more difficult to perform the dissection post-mortem. How
many different habitats do you think we are combining into a single sample when
we grind up entire insects? At least, from my perspective (Wolbachia-centric and all), the
reproductive tract and the gut are distinct and that’s not considering possible
specialized organs. When we sampled the
honey bee gut vs. the entire body of the bee, we saw considerable differences
in terms of both diversity sampled and in the composition of the community,
largely due to the rank abundance distribution of the reads and the ability to
deeply sample the gut vs. a diverse set of habitats on the honey bee [4]. Now consider that in the Jones
et al study, we got 500 sequences from each insect. Do we still
think it reasonable to perform the kind of frequency-based OTU-culling
performed here and by others?

Consider that for sampling the human microbiome, we very deeply probe – 2,000 sequences or
more – individual stool samples (containing a subset of the gut, at best). How
many OTUs do you think we would find if we ground up an entire human and used
1g of that homogenate to run a PCR and produced 500 sequences?

To be fair, the authors are aware of the sampling limitation of
their work – the fact that they used whole insects – and address it head on
here:

“Our research, however, has two
limitations that may restrict our ability to detect the effect of diet on
bacterial community composition: 1) the broad diversity of insect samples, and
2) the use of whole insects. A study focused on a less diverse group of insects
with varied diets may be better suited to resolve the effects of taxonomy and
diet on bacterial communities. By using whole insects, we maximized our
detection of insect-associated bacteria but also included endosymbionts (i.e.
intracellular bacteria) in our analyses.”

I would argue that by using whole insects they maximized their
ability to detect very abundant
symbionts but they do not sample all habitats on the host evenly.

What
does frequency-based culling do to our data? Why do it?

One of the first studies that explored potential contaminants in
the “rare-biosphere” is the E.coli sequencing
paper by Kunin et al published back in 2009 [5]. In that study, the authors sequence a
laboratory strain of E. coli (a mock
community of a single organism with multiple rRNA operons). They find multiple OTUs that are not
classified as E. coli and pass the QC
filtration steps used by others previously.
In their own words:

"These contaminants represent only 0.03% of the reads
obtained in the present study and suggest that all PCR-based surveys that use
broad-specificity primers will likely suffer from similar low-level background
contamination, a point worth bearing in mind when interpreting rare biosphere
data."[5]

How do we incorporate these results in our data interpretation
from natural communities? How do we bias our dataset when we set an arbitrary
threshold for how many times an OTU must occur before we consider it “real”?

Several studies following the Kunin et al analysis chose not to
perform a frequency-based cull (or at least did not report it in their methods):
the Dominguez-Bello infant study used 2,000 reads per sample [6], the Caporaso study used
3.1 million reads, deciding that 2,000 was sufficient to characterize that
environment [7]. Some other authors have taken advantage of the
rank abundance curve to quality filter their reads without completely deleting
the rare biosphere [8], and others have
pointed out that severe amplicon noise filtration, and indeed any diversity
metric has to make some assumptions about the underlying population from which
we are sampling (an unknown) and that amplicon noise reduction algorithms may
not be based on actual read characteristics and can bias the data [9, 10].

All
noise filtration pipelines not strictly focused on Phred scores and
read-specific parameters make assumptions about the underlying, unsampled
community. By
removing OTUs that don’t exist at 1% or greater in your single sample you are
necessarily making an assumption about the diversity you expect to find in that
habitat and biasing the results. It’s
one thing to consider removing singletons - reads that occur only once and may be contamination - but quite another to consider only OTUs at a larger, arbitrary frequency/percentage.

One of the results in the Jones et al paper really highlights the
fact that they did not deeply sample their insects. Consider the termite (Isoptera:Termitidae)
called Coptotermes formosanus. This wood-eating termite is host to many
diverse bacteria and protists – it’s often used in microbiology courses
focusing on symbiosis due to the neat morphologies and behaviors you can
observe under the microscope after dissection of the hind gut. Others who have more deeply sampled the
termite gut (admittedly, a different species) have found between 300 - 700
bacterial OTUs present there (depending on the primer set used see [11]), and a metagenomic
sampling of Coptotermes formosanus itself
also revealed a large amount of bacterial diversity [12]. Although we don’t have the raw number of OTUs
found in the Coptotermes sample in
the Jones et al study you can see the average richness in Table 2 is listed as
5.83. This is extremely low based on our
understanding of the termite gut and expectations from these natural
environments.